Metadata-Version: 2.4
Name: django-explain-errors
Version: 0.3.0
Summary: Django middleware that captures errors and exceptions, sends them to OpenAI for a detailed explanation, and prints the explanation to stdout when debug mode is enabled. Supports both sync and async views.
Home-page: https://github.com/topunix/django-explain-errors
Author: topunix
Author-email: topunixguy@gmail.com
License: MIT
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Web Environment
Classifier: Framework :: Django
Classifier: Framework :: Django :: 4.2
Classifier: Framework :: Django :: 5.0
Classifier: Framework :: Django :: 5.1
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Internet :: WWW/HTTP
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: Django>=4.2
Requires-Dist: openai>=1.0
Requires-Dist: python-dotenv>=1.0
Requires-Dist: asgiref>=3.6
Provides-Extra: rag
Requires-Dist: sqlite-vec>=0.1.0; extra == "rag"
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license
Dynamic: license-file
Dynamic: provides-extra
Dynamic: requires-dist
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# Django Explain Errors Middleware

This Django middleware captures errors and exceptions, sends them to OpenAI for explanation, and prints the explanation to stdout when debug mode is enabled. It can optionally ground explanations in your own project source code using a local vector index (RAG), so explanations reference the actual code that failed instead of staying generic.

The middleware supports both synchronous (WSGI) and asynchronous (ASGI) views. It auto-detects the view chain at startup and routes requests through the matching sync or async path, so no extra configuration is required to use it under either server type. Tracebacks are sanitized before leaving the process, and API calls are rate limited. It uses an environment variable to securely manage the OpenAI API key.

## Features

- Captures Django errors and exceptions
- Uses OpenAI to explain the error
- Optional codebase-aware explanations (RAG) backed by a local sqlite-vec index (see the RAG section below)
- Redacts secrets, tokens, and emails from tracebacks before sending
- Rate limits API calls with a configurable sliding window
- Works with both sync (WSGI) and async (ASGI) views
- Securely manages the OpenAI API key using environment variables

## Installation

1. Install django-explain-errors by running:
```bash
pip install django-explain-errors
```

2. **Add the middleware to your Django project**:

   - Open your `settings.py` file and add the middleware to the `MIDDLEWARE` list. Ensure that the middleware is added last in the list:

     ```python
     MIDDLEWARE = [
         ...
         'explain_errors.middleware.ExplainErrorsMiddleware',
     ]
     ```

3. **Set up environment variables**:

   - Create a `.env` file in your project's root directory and add your OpenAI API key. Alternatively, you can set the API key in `settings.py`:

     ```plaintext
     OPENAI_API_KEY=your_openai_api_key_here
     ```

## Usage

1. **Ensure DEBUG is set to True**:

   Open your `settings.py` file and set:

   ```python
   DEBUG = True
   ```

2. **Trigger an error in your Django application**:

   The middleware will capture the error, send it to OpenAI for explanation, and print the explanation to stdout. When an exception is caught, it returns a JSON `500` response containing the error message and the explanation.

## Async Support

The middleware exposes both `sync_capable = True` and `async_capable = True`. At initialization it inspects `get_response` to decide whether it is part of a sync or async chain:

- Under WSGI (for example `runserver` with sync views), requests flow through the synchronous handler.
- Under ASGI (for example with async views), requests are awaited through the async handler. The blocking OpenAI call is offloaded with `asgiref.sync.sync_to_async` so the event loop is not blocked.

No additional settings are needed. Place the middleware last in `MIDDLEWARE` for both modes.

## Configuration

| Setting / variable | Required | Description |
| ------------------ | -------- | ----------- |
| `OPENAI_API_KEY` (env or settings) | Yes, when `DEBUG=True` | API key used to authenticate with OpenAI. Read first from the environment, then from `settings`. |
| `DEBUG` | Yes | The middleware is only active when `DEBUG=True`. When `False`, requests pass through untouched. |
| `OPENAI_MODEL` | No | Model used for explanations. Defaults to `gpt-4o-mini`. |
| `OPENAI_MAX_TOKENS` | No | Maximum tokens in the explanation. Defaults to `150`. |
| `OPENAI_TIMEOUT` | No | Request timeout in seconds for the OpenAI client. Defaults to `10`. |
| `OPENAI_MAX_TRACEBACK_CHARS` | No | Traceback is trimmed to its last N characters before being sent. Defaults to `3000`. |

## Codebase-aware explanations (RAG)

By default, explanations are generated from the traceback alone. With the
optional RAG (retrieval-augmented generation) layer enabled, the middleware
also retrieves the most relevant chunks of your own project's source code
from a local vector index and includes them in the prompt, so explanations
can reference your actual functions and classes instead of guessing at them.

This feature is opt-in and adds no dependencies or behavior unless enabled.

### Install the extra

```bash
pip install django-explain-errors[rag]
```

This pulls in [sqlite-vec](https://github.com/asg017/sqlite-vec), a
single-file, no-server vector store. The core package stays dependency-light
if you don't need RAG.

### Build the index

Add `explain_errors` to `INSTALLED_APPS` (needed for Django to discover the
management command), then run:

```bash
python manage.py build_error_index
```

This walks your project, chunks Python files by top-level function/class
(and other text files by fixed-size line windows), embeds each chunk with
the OpenAI embeddings API, and writes them to a local index file. Re-run it
whenever your source changes meaningfully — indexing is not automatic.
Rebuilding is idempotent: it builds into a temp file and atomically replaces
the previous index.

### Enable it

```python
# settings.py

EXPLAIN_ERRORS_RAG_ENABLED = True
```

### Settings

| Setting | Default | Description |
| ------- | ------- | ----------- |
| `EXPLAIN_ERRORS_RAG_ENABLED` | `False` | Master switch for the RAG layer. |
| `EXPLAIN_ERRORS_RAG_INDEX_PATH` | `<BASE_DIR>/.explain_errors_index.db` | Path to the local vector index file. |
| `EXPLAIN_ERRORS_RAG_TOP_K` | `4` | Number of chunks retrieved and injected into the prompt. |
| `EXPLAIN_ERRORS_RAG_EMBED_MODEL` | `"text-embedding-3-small"` | OpenAI embedding model used for indexing and retrieval. |
| `EXPLAIN_ERRORS_RAG_INCLUDE` | `None` (defaults to `BASE_DIR`) | List of directories to index. |
| `EXPLAIN_ERRORS_RAG_EXCLUDE` | migrations, venvs, `node_modules`, static, media, `.git` | Directory names to skip while indexing. |
| `EXPLAIN_ERRORS_RAG_MAX_PROMPT_CHARS` | `6000` | Combined character budget for the traceback + retrieved source sections of the prompt. |

Every chunk of source code and every retrieval query is passed through the
same `sanitize_traceback()` redaction used for tracebacks, so secrets in
your source files are never sent to OpenAI or written to the index.

If RAG is enabled but the index is missing, `sqlite-vec` isn't installed, or
retrieval fails for any reason, the middleware logs a warning and falls back
to the traceback-only prompt — it never breaks error reporting.

RAG-grounded explanations tend to be longer than traceback-only ones. Consider raising `OPENAI_MAX_TOKENS` (for example to 500) when RAG is enabled so explanations are not truncated.

### .gitignore

The index file is a local build artifact, not something to commit. Add it
to your project's `.gitignore`:

```
.explain_errors_index.db
```

(Adjust the path if you set `EXPLAIN_ERRORS_RAG_INDEX_PATH` to something
else.)

### Before / after

**Without RAG** — traceback only:

> Your `ValueError` is raised because the value passed to `foo()` couldn't
> be converted to an integer. Check where `foo()` is called and make sure
> you're passing a numeric string.

**With RAG** — grounded in the actual function:

> In `myapp/utils.py`, `foo()` calls `int(value)` on line 12 without a
> `try`/`except`, so any non-numeric `value` raises `ValueError` straight
> through to the caller. Since `foo()` is called from `myapp/views.py` with
> unvalidated form input, add validation there or wrap the `int()` call in
> `foo()` with a clear error message.

## Example

Here is an example of how to use the middleware in a Django project:

```python
# settings.py

DEBUG = True

MIDDLEWARE = [
    ...
    'explain_errors.middleware.ExplainErrorsMiddleware',
]

# .env

OPENAI_API_KEY=your_openai_api_key_here
```

When an error occurs, you will see an explanation printed to stdout.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

## Acknowledgements

- [Django](https://www.djangoproject.com/)
- [OpenAI](https://www.openai.com/)
- [python-dotenv](https://github.com/theskumar/python-dotenv)
